Two design points: workflows chain LLM calls and tools along predetermined paths in your code; agents let the model dynamically direct its own steps and tool use toward a goal.
How it works
A Workflow is a structured sequence of operations that you define in your code. Think of it as a series of predetermined steps where each call to a language model (LLM) or tool follows a fixed control flow. For instance, you might set up a workflow to classify user input, route it to the appropriate processing tool, and then generate a response based on that input. Each step is clearly defined, and the order in which they execute does not change. This predictability makes workflows easy to test and debug, as you know exactly what will happen at each stage.
On the other hand, an Agent introduces a level of dynamism into the process. Instead of following a predetermined path, the agent uses the LLM to assess the current situation and decide which tools to use and what actions to take next. This decision-making happens in a loop, allowing the agent to adapt its approach based on the context it encounters. For example, if the agent is tasked with answering a complex user query, it might first gather information, then choose to consult additional resources or tools based on what it learns along the way. This flexibility can be powerful but also makes the flow less predictable.
In practice, most real-world systems blend these two approaches. You might have a deterministic workflow that handles the bulk of the processing, while incorporating an agent at specific points where flexibility is crucial. This combination allows you to maintain control over the overall process while still enabling the model to adapt and respond to unforeseen challenges or opportunities.
- Use workflows for fixed paths.
- Choose agents for dynamic tasks.
- Start with workflows when unsure.
- Workflows enable easy testing.
- Agents introduce unpredictability.
The software-engineering analogue
In software engineering, you can think of a workflow as a hardcoded pipeline or a directed acyclic graph (DAG), where each node represents a specific operation that must occur in a fixed order. This is similar to a traditional function call in programming, where you know exactly what will happen when you call that function. In contrast, an agent resembles a rules engine that makes decisions at runtime, allowing for a more fluid and responsive approach. The key difference here is that while workflows are deterministic, agents rely on a nondeterministic model, meaning that the path taken can vary from one execution to another based on the agent's decisions.
Why not the obvious alternative
Many newcomers are drawn to the idea of using agents because they seem more flexible and capable of handling complex tasks. However, this autonomy comes with trade-offs. Agents can introduce unpredictability, making it harder to test and debug the system. They may also incur higher latency and costs, as the model must evaluate options in real-time rather than following a straightforward path. For tasks that have a clear and knowable structure, such as data classification, a workflow is often the more efficient choice.
Anthropic's guidance emphasizes the importance of simplicity: use the simplest solution that meets your needs. When the steps in a task are fixed and well-defined, a workflow is preferable. An agent should only be employed when the task genuinely requires the ability to make runtime decisions that cannot be pre-scripted. By consciously choosing between control and flexibility, you can ensure that your system is designed to meet its specific requirements without unnecessary complications.
When to use which
Use a workflow when the path through your process is known and repeatable, such as a sequence that involves classifying input, routing it to the right tool, and generating a response. This approach is efficient and straightforward, making it easy to manage and troubleshoot. On the other hand, opt for an agent when the path depends on discoveries made during the task and cannot be easily scripted. If you're unsure which approach to take, starting with a workflow is often a good strategy. You can always introduce agent-based elements later if you find that flexibility is needed.
In production: when it breaks
Why it happens. The increase in response time can occur when the workflow encounters unexpected conditions that lead to inefficient branching or excessive calls to external tools. For example, if the workflow is not designed to handle certain user inputs gracefully, it may loop or retry excessively, causing delays.